Geochemical equilibrium determination using an artificial neural network in compositional reservoir flow simulation

DOI10.1007/s10596-019-09861-4zbMath1434.80012OpenAlexW2989779206WikidataQ126647696 ScholiaQ126647696MaRDI QIDQ2185985

Jérémie Bruyelle, Dominique Guérillot

Publication date: 8 June 2020

Published in: Computational Geosciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10596-019-09861-4

zbMATH Keywords

heterogeneityreservoir simulationartificial neural networkcompositionalchemically reacting flows\(\mathrm{CO_2}\) storage

Mathematics Subject Classification ID

Artificial neural networks and deep learning (68T07) Flows in porous media; filtration; seepage (76S05) Chemically reacting flows (80A32)

Related Items (2)

An active learning approach for improving the performance of equilibrium based chemical simulations ⋮ Optimization of subsurface \(\mathrm{CO_2}\) injection based on neural network surrogate modeling

Uses Software

• HYDROGEOCHEM
• TOUGHREACT
• PFLOTRAN
• CrunchFlow

Cites Work

• On macroscopic equations governing multiphase flow with diffusion and chemical reactions in porous media
• Reactive transport codes for subsurface environmental simulation
• Multilayer feedforward networks are universal approximators
• Compositional dual mesh method for single phase flow in heterogeneous porous media -- application to CO\(_2\) storage
• On the Construction and Comparison of Difference Schemes
• Approximation by superpositions of a sigmoidal function

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